Behavioral Data Science I

Catalog description

Introduces social and behavioral scientists to modern data science using R, focusing on data analysis, visualization, collection, cleaning, statistical modeling and effective communication. Requires no prior computing or statistical skills. Students start by learning to manipulate and transform data, create and recode variables and understand data structures in R. Develops critical thinking skills to evaluate data, identify biases and create visualizations. Covers debugging, code organization, documentation and using R Markdown for reproducible analysis. Advanced topics include complex visualizations, causal relationships and fitting linear models. Students learn the tidymodels framework for building and evaluating statistical and machine learning models. Applies these skills to predict outcomes and extrapolate from data. A key component is designing an individual data science project, involving formulating research questions, finding and preparing data, visualizing data and communicating analyses. This project includes a proposal, in-class pitches, peer reviews and a final R Markdown document. By the end, students have robust data science skills tailored to social and behavioral sciences, ready to tackle complex problems and communicate findings effectively.